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- W2899277848 abstract "One of the ways of reducing the effects of Climate Change is to rely on renewable energy sources. Their intermittent nature makes necessary to obtain a mid-long term accurate forecasting. Wind Energy prediction is based on the ability to forecast wind speed. This has been a problem approached using different methods based on the statistical properties of the wind time series.Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting obtaining a 12 hours ahead prediction using data from the National Renewable Energy Laboratory's WIND dataset" @default.
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- W2899277848 date "2018-01-01" @default.
- W2899277848 modified "2023-09-23" @default.
- W2899277848 title "Go with the flow: Recurrent networks for wind time series multi-step forecasting" @default.
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- W2899277848 doi "https://doi.org/10.3233/978-1-61499-918-8-79" @default.
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